R. Adams Cowley

Background: Large language models (LLMs) have demonstrated increasing capabilities in generating clinically coherent and accurate responses to patient questions, in some cases outperforming physicians in terms of accuracy and empathy. However, little is known about how physicians across geographic regions and levels of clinical experience evaluate these artificial intelligence (AI)–generated responses compared to those authored by human clinicians. Objective: This study examined physician evaluations of LLM-generated versus physician-authored responses to real-world patient questions, comparing preference patterns across geographic regions and years in clinical practice. Methods: We conducted a cross-sectional online survey between March and May 2025 among licensed physicians recruited internationally. Participants reviewed anonymized medical responses from 2 LLMs (GPT-4.0 and Meta AI) and verified physicians to questions sourced from Reddit’s r/AskDocs forum. Each participant ranked 3 responses per question (1=most preferred; 3=least preferred) according to accuracy and responsiveness. Mean ranks, pairwise win proportions, and full rank distributions were analyzed descriptively and stratified by geographic region and years in practice. Results: Overall, LLM-generated responses were strongly preferred. GPT-4.0 achieved the best mean rank (1.63, SD 0.68; 95% CI 1.52-1.74), followed by Meta AI (1.83, SD 0.72; 95% CI 1.71-1.94), while verified physician-authored responses were least preferred (2.53, SD 0.76; 95% CI 2.40-2.65). In pairwise analyses, responses generated by GPT-4.0 won 78% (118/150) of the head-to-head comparisons versus physician-authored responses and 57% (86/150) versus Meta AI responses. Preference for GPT-4.0 was most pronounced in Africa (mean 1.59, SD 0.72), Asia (mean 1.91, SD 0.83), and North America (mean 1.55, SD 0.60), while Meta AI slightly led in Europe (mean 1.33, SD 0.57) and the Americas (mean 1.75). Across experience levels, physicians with less than 5 years in practice (28/52, 54%) ranked GPT-4.0 most favorably (mean 1.58, SD 0.63), followed by those with 10 to 15 years of experience (mean 1.56, SD 0.72). Even among physicians with more than 15 years in practice (9/52, 17%), AI-generated responses outperformed physician-authored responses (mean 1.75 vs 2.62). Across all subgroups, human-authored responses were ranked lowest. Conclusions: This exploratory study demonstrates that physicians across diverse regions and experience levels generally prefer LLM-generated responses to human-authored ones. The consistency of this finding across continents and practice durations underscores growing professional acceptance of AI as a viable tool for patient communication. These results suggest that modern LLMs, particularly GPT-4.0, may provide clinically acceptable, contextually relevant, and user-trusted health information, with potential to augment physician workflows and patient education.

Background: Large language models (LLMs) have demonstrated a unique ability to generate clinically accurate responses to patient questions, in some cases outperforming physicians. However, little is known about how physician evaluations of such responses vary globally and by years in clinical practice. Objective: This study builds on prior work by comparing LLM-generated and physician-authored responses to patient questions using two general-purpose LLMs in an international sample of physicians. Participants were asked to rank responses based on accuracy and responsiveness. Methods: We conducted a survey to assess physician preferences for AI- and human-generated responses to patient questions from the r/AskDocs subreddit. Participants reviewed anonymized answers from ChatGPT-4.0, Meta.AI, and a verified physician, ranking each from best (1) to worst (3). We summarized respondent characteristics descriptively. The primary outcome was the mean rank of each response type. Sensitivity analyses included pairwise win proportions and full rank distribution visualizations. Results: Fifty-two physicians completed the survey, most of whom were male (78.8%), aged 25–34 (53.8%), based in North America (48.1%) or Africa (25.0%), and over half (53.8%) had less than 5 years of clinical experience. Across all regions, ChatGPT-4.0 and Meta.AI responses were preferred over physician-authored responses, with ChatGPT-4.0 ranked highest in Africa, Asia, Asia Pacific, and North America, and Meta.AI slightly favored in Europe and the Americas. By years in practice, AI-generated responses consistently outperformed physician responses, with ChatGPT-4.0 most preferred among those with less than 15 years of experience and showing the greatest advantage in the 10– 15 year group. Conclusions: In our global sample, most physicians preferred LLM-generated responses over those written by human contributors. However, preferences varied by geographic region and years in clinical practice, suggesting that both cultural and experiential factors shape physician attitudes toward Artificial Intelligence (AI). These preliminary findings highlight the need for larger, adequately powered studies to assess statistically significant differences and interactions across subgroups. Such research is essential to inform context-specific strategies for integrating AI into patient-facing communication. Trial Registration: N/A

BACKGROUND Large language models (LLMs) have demonstrated increasing capabilities in generating clinically coherent and accurate responses to patient questions, in some cases outperforming physicians in terms of accuracy and empathy. However, little is known about how physicians across geographic regions and levels of clinical experience evaluate these artificial intelligence (AI)–generated responses compared to those authored by human clinicians. OBJECTIVE This study examined physician evaluations of LLM-generated versus physician-authored responses to real-world patient questions, comparing preference patterns across geographic regions and years in clinical practice. METHODS We conducted a cross-sectional online survey between March and May 2025 among licensed physicians recruited internationally. Participants reviewed anonymized medical responses from 2 LLMs (GPT-4.0 and Meta AI) and verified physicians to questions sourced from Reddit’s r/AskDocs forum. Each participant ranked 3 responses per question (1=most preferred; 3=least preferred) according to accuracy and responsiveness. Mean ranks, pairwise win proportions, and full rank distributions were analyzed descriptively and stratified by geographic region and years in practice. RESULTS Overall, LLM-generated responses were strongly preferred. GPT-4.0 achieved the best mean rank (1.63, SD 0.68; 95% CI 1.52-1.74), followed by Meta AI (1.83, SD 0.72; 95% CI 1.71-1.94), while verified physician-authored responses were least preferred (2.53, SD 0.76; 95% CI 2.40-2.65). In pairwise analyses, responses generated by GPT-4.0 won 78% (118/150) of the head-to-head comparisons versus physician-authored responses and 57% (86/150) versus Meta AI responses. Preference for GPT-4.0 was most pronounced in Africa (mean 1.59, SD 0.72), Asia (mean 1.91, SD 0.83), and North America (mean 1.55, SD 0.60), while Meta AI slightly led in Europe (mean 1.33, SD 0.57) and the Americas (mean 1.75). Across experience levels, physicians with less than 5 years in practice (28/52, 54%) ranked GPT-4.0 most favorably (mean 1.58, SD 0.63), followed by those with 10 to 15 years of experience (mean 1.56, SD 0.72). Even among physicians with more than 15 years in practice (9/52, 17%), AI-generated responses outperformed physician-authored responses (mean 1.75 vs 2.62). Across all subgroups, human-authored responses were ranked lowest. CONCLUSIONS This exploratory study demonstrates that physicians across diverse regions and experience levels generally prefer LLM-generated responses to human-authored ones. The consistency of this finding across continents and practice durations underscores growing professional acceptance of AI as a viable tool for patient communication. These results suggest that modern LLMs, particularly GPT-4.0, may provide clinically acceptable, contextually relevant, and user-trusted health information, with potential to augment physician workflows and patient education.

Case: Primary spinal epidural lymphoma (PSEL) presenting as myelopathy is extremely rare, particularly within young, healthy adults. This case report describes a 26-year-old man presenting with progressive thoracic myelopathy. Magnetic resonance imaging revealed spinal epidural masses spanning T5-T10 and T12-L2 with multilevel cord compression and edema. After evaluation, the patient underwent emergent posterior decompression to prevent progressive neurological decline. Histology was consistent with diffuse large B-cell lymphoma, germinal center type. At 3 months postoperatively, the patient regained full neurologic function. Conclusion: Although rare, PSELs should be considered in patients presenting with myelopathy to facilitate timely diagnosis and treatment.